Modeling the Modal Shift towards a More Sustainable Transport by Stated Preference in Riyadh, Saudi Arabia
Bibliographic record
Abstract
The need to gain a comprehensive understanding of road travelers’ choice of mode and their perceptions of using sustainable urban mobility modes have evolved to shape the form of future transport planning and policymaking. To combat the concern of growing traffic congestion in Riyadh City, the government of Saudi Arabia designed and introduced a sustainable public transport project named “Riyadh Metro”. This study explores the potential commuters’ perception towards the Metro services and the factors that limit their propensity to use Metro and understand the tradeoffs that the individuals make when they are faced with a combination of mode characteristics (e.g., travel time, price, walking time). The stated preferences experiment was conducted on a sample from the Riyadh neighborhood by structured interviews. A discrete choice model based on binary logistic regression has been developed. The coefficient of travel attribute: travel time, fuel cost, Metro fare, and walking time was found to be statistically significant with a different effect on mode choice. The elasticity of the coefficient showed that an increase in the fuel price by 10% would increase the metro ridership by 5.3% and reduce car dependency. Decreasing the walking time by 5 min to the metro station will increase the metro ridership by 22%. Furthermore, the study revealed that implementing a 1 SAR/hour parking charge will decrease car dependency by 14%. Increase Metro fare by 10% will decrease Metro ridership by 6.9%. The socioeconomic factors coefficient shows a marginal effect on the choice decision of passengers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".